Evidence map›Paper›PMID 34444028›Full record

ArticleInternational journal of environmental research and public health2021

Study on Multi-Agent Evolutionary Game of Emergency Management of Public Health Emergencies Based on Dynamic Rewards and Punishments.

Ruguo Fan, Yibo Wang, Jinchai Lin

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  11. Agricultural Openness and the Risk of COVID-19 Incidence: Evidence from China.International journal of environmental research and public health · 2022
    Article
  12. Article
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  15. Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic.International journal of environmental research and public health · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ruguo FanSchool of Economics and Management, Wuhan University, Wuhan 430072, China.
Yibo WangSchool of Economics and Management, Wuhan University, Wuhan 430072, China.ORCID 0000-0001-8097-5200
Jinchai LinSchool of Economics and Management, Wuhan University, Wuhan 430072, China.

Funding

Major Program of National Fund of Philosophy and Social Science of China 20&ZD155Ministry of education in the later stage of philosophy and social science research 19JHG091National Natural Science Foundation of China 72061003
6 · The paper itself

Abstract

In the context of public health emergency management, it is worth studying ways to mobilize the enthusiasm of government, community, and residents. This paper adopts the method of combining evolutionary game and system dynamics to conduct a theoretical modeling and simulation analysis on the interactions of the behavioral strategies of the three participants. In response to opportunistic behavior and inadequate supervision in the static reward and punishment mechanism, we introduced a dynamic reward and punishment mechanism that considers changes in the social environment and the situation of epidemic prevention and control. This paper proves that the dynamic reward and punishment mechanism can effectively suppress the fluctuation problem in the evolutionary game process under static scenarios and achieve better supervision results through scenario analysis and simulation experiments.

Indexed as

Public HealthPunishmentBiological EvolutionCooperative BehaviorEmergenciesHumansRewardCOVID-19evolutionary gameregulationsubsidy and punishment mechanism

Identifiers

PMID34444028
PMCPMC8391147

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.